Prediction of Corporate Bankruptcy using Financial Ratios and News

  • Isha Arora
  • Navjot Singh
Keywords: Bankruptcy Prediction, Financial News, Financial Sentiment Analysis, News and Ratios

Abstract

A corporate’s insolvency can have catastrophic effects on not only the corporate but also on the returns of its lenders and investors. Predicting bankruptcy has been one of the most sought-after areas for researchers for many decades. This study involves predicting the bankruptcy of the United States corporates using financial ratios and news data. The financial ratios of the companies were extracted from yearly financial reports of the companies, and the news data of the companies was scrapped from online newspapers, reports and articles using Google News. The news data was analyzed for negative and positive sentiments. The sentiment scores, along with the financial ratios of the companies, were given as features to the machine learning models. Various models were analyzed for their results such as Random Forest, Logistic Regression and Support Vector Machines (SVM). The study finds the best results from the random forest model with an accuracy of 90%. Moreover, the significant feature importance of the sentiment score given by the model proves that unstructured data, such as news, can play a crucial part in predicting bankruptcy in conjunction with the structured data, such as financial ratios.

Downloads

Download data is not yet available.

References

Ahmadi, Z., Martens, P., Koch, C., Gottron, T., & Kramer, S. (2018). Towards bankruptcy prediction: Deep sentiment mining to detect financial distress from business management reports. IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), Turin, Italy, pp. 293-302. Available at: https://doi.org/10.1109/DSAA.2018.00040.

Ahn, B.S., Cho, S.S., & Kim, C.Y. (2000). The integrated methodology of rough set theory and artificial neural network for business failure prediction. Expert Systems with Applications 18, 65-74.

Altman, E.I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589–609.

Beaver, W. (1966). Financial ratios as predictors of failure, empirical research in accounting, selected studies. Journal of Accounting Research, 4, 71-111.

Chaovalit, Pimwadee & Zhou, L. (2005). Movie review mining: A comparison between supervised and unsupervised classification approaches. Proceedings of the 38th Annual Hawaii International Conference on System Sciences, pp. 112c-112c.

Gepp, Adrian & Kumar, Kuldeep. (2008). The role of survival analysis in financial distress prediction. International Research Journal of Finance and Economics 16, 13-34.

Hajek, Petr, Olej, Vladimir & Myskova, Renata. (2014). Forecasting corporate financial performance using sentiment in annual reports for stakeholders’ decision-making. Technological and Economic Development of Economy, 20(4), 721-738. Available at: https://doi.org/10.3846/20294913.2014.979456.

Hájek, Petr & Olej, Vladimír. (2015). Word categorization of corporate annual reports for bankruptcy prediction by machine learning methods. Proceedings of the 18th International Conference on Text, Speech, and Dialogue 9302. Springer-Verlag, Berlin, Heidelberg, pp. 122–130. Available at: https://doi.org/10.1007/978-3-319-24033-6_14.

Lee, Ming-Chang. (2014). Business bankruptcy prediction based on survival analysis approach. International Journal of Computer Science and Information Technology, 6, 103-119. Available at: https://doi.org/10.5121/ijcsit.2014.6207.

Li, N. & Wu, D. D. (2010). Using text mining and sentiment analysis for online forums hotspot detection and forecast. Decision Support Systems, 48(2), 354–368.

Li, X., Xie, H., Chen, L., Wang, J., & Deng, X. (2014). News impact on stock price return via sentiment analysis. Knowledge-Based Systems, 69, 14–23.

Lin, F.Y. McClean, & S.I. (2001). A data mining approach to the prediction of corporate failure. Knowledge-Based Systems 14, (3-4), 189-195. Available at: https://doi.org/10.1016/S0950-7051(01)00096-X.

Loughran, T. and McDonald, B. 2011. When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10‐Ks. The Journal of Finance 66, 35-65. Available at: https://doi.org/10.1111/j.1540-6261.2010.01625.x.

Miniwatts Marketing Group. (2020). Internet world stats: Usage and population statistics. Viewed 19 October, 2010. Available at: https://www.internetworldstats.com/emarketing.htm.

Odom, Marcus & Sharda, Ramesh. (1990). A neural network model for bankruptcy prediction. IEEE International Joint Conference on Neural Networks, 2, 163 – 168. Available at: https://doi.org/10.1109/IJCNN.1990.137710.

Ohlson, J. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18, 109-131.

O’Leary, E. G. (2000). Business failure prediction and the efficient market hypothesis. Simon Fraser University. (Parker et al. 2002) Parker, S., Peters, G. F., and Turetsky, H. F. (2002). Corporate governance and corporate failure: a survival analysis. Corporate Governance. The International Journal of Business in Society, 2(2), 4–12.

Prusak, Błaz˙ej. (2005). Modern methods of forecasting financial risk of enterprises. Warszawa: Difin.

Siddiqui, Sanobar. (2012). Business bankruptcy prediction models: A significant study of the Altman’s Z-score model. SSRN Electronic Journal. Available at: https://doi.org/10.2139/ssrn.2128475.

Published
2020-10-31
How to Cite
Isha Arora, & Navjot Singh. (2020). Prediction of Corporate Bankruptcy using Financial Ratios and News. International Journal of Engineering and Management Research, 10(5), 82-87. https://doi.org/10.31033/ijemr.10.5.15